Improving Scalability in Traffic Engineering via Optical Topology Programming
Improving Scalability in Traffic Engineering via Optical Topology Programming
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DOI：
10.1109/tnsm.2023.3335898
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发表时间：
2024-04
影响因子：
5.3
通讯作者：
Matthew Nance-Hall;P. Barford;Klaus-Tycho Foerster;Ramakrishnan Durairajan
中科院分区：
文献类型：
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作者：
Matthew Nance-Hall;P. Barford;Klaus-Tycho Foerster;Ramakrishnan Durairajan
我们提出了一种新的框架，GreyLambda，以提高流量工程（TE）系统的可扩展性。TE系统持续监控流量并根据观察到的需求分配网络资源。TE的时间要求是在五分钟或更短的时间内解决问题。此外，流量分配具有空间要求，即使所有流量能够穿越网络而不会遇到超额预订的链路。然而，基于多商品流的TE公式不能随着网络规模的增加而扩展。最近的方法放宽了多商品流的限制，以满足时间的要求，但不能满足空间的要求，由于不断变化的交通需求，导致超额认购的链接或不可行的解决方案。为了满足这两个要求，我们利用光拓扑编程（OTP），快速重新配置关键网络路径中的光波长，并提供本地化的带宽扩展和新的路径流量转发。GreyLambda通过引入启发式算法将OTP集成到TE系统中，该启发式算法利用高度节点处的潜在硬件资源来提供带宽缩放，以及减少光路径重新配置延迟的方法。我们的实验表明，GreyLambda增强了两个国家的最先进的TE系统，SMORE和NCFlow在现实世界的拓扑结构具有挑战性的流量和链路故障的情况下的性能。
We present a novel framework, GreyLambda, to improve the scalability of traffic engineering (TE) systems. TE systems continuously monitor traffic and allocate network resources based on observed demands. The temporal requirement for TE is to have a time-to-solution in five minutes or less. Additionally, traffic allocations have a spatial requirement, which is to enable all traffic to traverse the network without encountering an over-subscribed link. However, the multi-commodity flow-based TE formulation cannot scale with increasing network sizes. Recent approaches have relaxed multi-commodity flow constraints to meet the temporal requirement but fail to satisfy the spatial requirement due to changing traffic demands, resulting in oversubscribed links or infeasible solutions. To satisfy both these requirements, we utilize optical topology programming (OTP) to rapidly reconfigure optical wavelengths in critical network paths and provide localized bandwidth scaling and new paths for traffic forwarding. GreyLambda integrates OTP into TE systems by introducing a heuristic algorithm that capitalizes on latent hardware resources at high-degree nodes to offer bandwidth scaling, and a method to reduce optical path reconfiguration latencies. Our experiments show that GreyLambda enhances the performance of two state-of-the-art TE systems, SMORE and NCFlow in real-world topologies with challenging traffic and link failure scenarios.